A major goal in genetics research is to understand how sequence variations in the genome relate to complex traits, particularly susceptibilities for common diseases such as diabetes, cancer, hypertension, and the like, e.g. Collins et al, Nature, 422: 835-847 (2003). The draft sequence of the human genome has provided a highly useful reference for assessing variation, but it is only a first step towards understanding how the estimated 10 million or more common single nucleotide polymorphisms (SNPs), and other polymorphisms, such as inversions, deletions, insertions, and the like, determine or affect states of health and disease. Many powerful analytical approaches have been developed to address this problem, but none appear to have adequate throughput or flexibility for the types of studies required to associate traits practically and reliably with genomic variation, e.g. Syvanen, Nature Reviews Genetics, 2: 930-942 (2001). For example, it would be desirable to carry out trait-association studies in which a large set of genetic markers from populations of affected and unaffected individuals are compared. Such studies depend on the non-random segregation, or linkage disequilibrium, between the genetic markers and genes involved in the trait or disease being studied. Unfortunately, the extent and distribution of linkage disequilibrium between regions of the human genome is not well understood, but it is currently believed that successful trait-association studies in humans would require the measurement of 30-50,000 markers per individual in populations of at least 300-400 affected individuals and an equal number of controls, Kruglyak and Nickerson, Nature Genetics, 27: 234-236 (2001); Lai, Genome Research, 11: 927-929 (2001); Risch and Merikangas, Science, 273: 1516-1517 (1996); Cardon and Bell, Nature Reviews Genetics, 2: 91-99 (2001).
One approach to dealing with such whole-genome studies is to create subsets of genomic DNA having reduced complexity with respect to the genomes being analyzed in order to simplify the analysis, e.g. Lisitsyn et al, Science, 259: 946-951 (1993); Vos et al, Nucleic Acids Research, 23: 4407-4414 (1995); Dong et al., Genome Research, 11: 1418-1424 (2001); Jordan et al, Proc. Natl. Acad. Sci., 99: 2942-2947 (2002); Weissman et al, U.S. Pat. No. 6,506,562; Sibson, U.S. Pat. No. 5,728,524; Degau et al, U.S. Pat. No. 5,858,656. Unfortunately, most of these techniques rely on some form of subtraction, sequence destruction, or direct or indirect size selection to create subsets, which are difficult to implement and reduce sensitivity.
In view of the above, the field of genetic analysis would be advanced by the availability of a method for converting a highly complex population of DNA, such as a genome or mixture of genomes, into subsets having reduced complexity without requiring subtraction, extraction or other sequence destroying steps.